In this paper, we propose a new context-sensitive Bayesian learning algorithm. By modeling the distributions of data locations by a mixture of Gaussians, the new algorithm can utilize different classifier complexities for different contexts/locations and, at the same time, keep the optimality of Bayesian solutions. This algorithm is also an online learning algorithm, efficient in training, and easy for incorporating new knowledge from data sets available in the future. We apply this algorithm to detecting computer-user mouse pressure patterns during episodes likely to be frustrating to the user. By modeling user identity as hidden context, this algorithm achieves on average 10.6% user-independent test error rate.